Module_04.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "caTiMNFpHZkQ"
      },
      "source": [
        "# Module 4 : Performance measurements of RF and SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DhRUqg_lHZkW"
      },
      "outputs": [],
      "source": [
        "#import library packages\n",
        "import pandas as p\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as s\n",
        "import numpy as n\n",
        "#read the given dataset\n",
        "df = p.read_csv(\"df4.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EYA1_4_dHZkZ"
      },
      "outputs": [],
      "source": [
        "df.rename(columns={'Mean Temp':'T'}, inplace=True)\n",
        "df.rename(columns={'Average Humidity':'H'}, inplace=True)\n",
        "df.rename(columns={'rainfall':'R'}, inplace=True)\n",
        "df.rename(columns={'Cost of Cultivation (`/Hectare) C2':'CC'}, inplace=True)\n",
        "df.rename(columns={'Cost of Production (`/Quintal) C2':'CP'}, inplace=True)\n",
        "df.rename(columns={'Yield (Quintal/ Hectare) ':'Y'}, inplace=True)\n",
        "df.rename(columns={'cost of production per yield':'CPPY'}, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8E1AigsYHZka"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "var_mod = ['Y']\n",
        "le = LabelEncoder()\n",
        "for i in var_mod:\n",
        "    df[i] = le.fit_transform(df[i]).astype(str)\n",
        "df['YPr']= df.Y.map({'13':0, '7':0, '11':0, '4':0, '23':0, '39':1, '10':0, '18':0, '36':1, '47':1, '8':0, '3':0,\n",
        "       '38':1, '46':1, '5':0, '9':0, '2':0, '44':1, '17':0, '41':1, '6':0, '16':0, '35':1, '19':0,\n",
        "       '0':0, '43':1, '12':0, '45':1, '25':0, '33':1, '29':0, '37':1, '32':1, '21':0, '42':1,\n",
        "       '48':1, '30':1, '34':1, '26':0, '20':0, '31':1, '24':0, '27':0, '22':0, '40':1, '28':0,\n",
        "       '1':0, '14':0, '15':0})\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GQc_jD6pHZkb",
        "outputId": "a7975e6a-ad40-40e1-bc5d-37c1d7438d0e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['State_Name', 'District_Name', 'Crop_Year', 'Season', 'Crop', 'Area',\n",
              "       'R', 'H', 'T', 'CC', 'CP', 'Y', 'CPPY', 'YPr'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o24mK-60HZkc",
        "outputId": "7c2a0abb-4fcd-4a3b-da9e-49f727c3ec2e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State_Name</th>\n",
              "      <th>District_Name</th>\n",
              "      <th>Crop_Year</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop</th>\n",
              "      <th>Area</th>\n",
              "      <th>R</th>\n",
              "      <th>H</th>\n",
              "      <th>T</th>\n",
              "      <th>CC</th>\n",
              "      <th>CP</th>\n",
              "      <th>Y</th>\n",
              "      <th>CPPY</th>\n",
              "      <th>YPr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1015</td>\n",
              "      <td>33</td>\n",
              "      <td>45</td>\n",
              "      <td>10</td>\n",
              "      <td>21</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>126</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1015</td>\n",
              "      <td>121</td>\n",
              "      <td>44</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>26</td>\n",
              "      <td>46</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1016</td>\n",
              "      <td>118</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "      <td>45</td>\n",
              "      <td>3</td>\n",
              "      <td>200</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1018</td>\n",
              "      <td>172</td>\n",
              "      <td>45</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>36</td>\n",
              "      <td>34</td>\n",
              "      <td>78</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1019</td>\n",
              "      <td>75</td>\n",
              "      <td>51</td>\n",
              "      <td>15</td>\n",
              "      <td>20</td>\n",
              "      <td>24</td>\n",
              "      <td>16</td>\n",
              "      <td>144</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  State_Name District_Name Crop_Year Season Crop  Area    R   H   T  CC  CP  \\\n",
              "0          0            42         3      0    0  1015   33  45  10  21  30   \n",
              "1          0            42         4      0    0  1015  121  44   6   3  26   \n",
              "2          0            42         5      3    0  1016  118  46   1  33  45   \n",
              "3          0            42         6      3    0  1018  172  45   6   4  36   \n",
              "4          0            42         7      3    0  1019   75  51  15  20  24   \n",
              "\n",
              "    Y CPPY  YPr  \n",
              "0   5  126    0  \n",
              "1  46   72    0  \n",
              "2   3  200    0  \n",
              "3  34   78    0  \n",
              "4  16  144    0  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "var_mod = ['State_Name', 'District_Name', 'Crop_Year', 'Season', 'Crop', 'Area',\n",
        "       'R', 'H', 'T', 'CC', 'CP', 'Y','CPPY']\n",
        "le = LabelEncoder()\n",
        "for i in var_mod:\n",
        "    df[i] = le.fit_transform(df[i]).astype(str)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "djjnHAZ-HZkd"
      },
      "outputs": [],
      "source": [
        "#According to the cross-validated MCC scores, the random forest is the best-performing model, so now let's evaluate its performance on the test set.\n",
        "from sklearn.metrics import confusion_matrix, classification_report, matthews_corrcoef, cohen_kappa_score, accuracy_score, average_precision_score, roc_auc_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1brn5945HZkd"
      },
      "source": [
        "# Prediction of Crop by yield"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P8O2kodcHZke"
      },
      "outputs": [],
      "source": [
        "X = df.drop(labels='YPr', axis=1)\n",
        "#Response variable\n",
        "y = df.loc[:,'YPr']    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qzkQRBG9HZkf"
      },
      "outputs": [],
      "source": [
        "#We'll use a test size of 30%. We also stratify the split on the response variable, which is very important to do because there are so few fraudulent transactions.\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9X6j6W6HZkg"
      },
      "source": [
        "Random Forest:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Y4bwljToHZkh",
        "outputId": "e3952706-2cc8-4d54-d6ec-0bb8ce205bd1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Classification report of Random Forest Results:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      1776\n",
            "           1       1.00      1.00      1.00      1087\n",
            "\n",
            "    accuracy                           1.00      2863\n",
            "   macro avg       1.00      1.00      1.00      2863\n",
            "weighted avg       1.00      1.00      1.00      2863\n",
            "\n",
            "Accuracy result of Random Forest is: 100.0\n",
            "\n",
            "Confusion Matrix result of Random Forest is:\n",
            " [[1776    0]\n",
            " [   0 1087]]\n",
            "\n",
            "Sensitivity :  1.0\n",
            "\n",
            "Specificity :  1.0\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc = RandomForestClassifier()\n",
        "\n",
        "rfc.fit(X_train,y_train)\n",
        "\n",
        "predictR = rfc.predict(X_test)\n",
        "\n",
        "print(\"\")\n",
        "print('Classification report of Random Forest Results:')\n",
        "print(\"\")\n",
        "\n",
        "print(classification_report(y_test,predictR))\n",
        "x = (accuracy_score(y_test,predictR)*100)\n",
        "\n",
        "print('Accuracy result of Random Forest is:', x)\n",
        "print(\"\")\n",
        "cm1=confusion_matrix(y_test,predictR)\n",
        "print('Confusion Matrix result of Random Forest is:\\n',cm1)\n",
        "print(\"\")\n",
        "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
        "print('Sensitivity : ', sensitivity1 )\n",
        "print(\"\")\n",
        "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
        "print('Specificity : ', specificity1)\n",
        "print(\"\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DgaDZo1vHZkj",
        "outputId": "70dc132c-50d4-422c-b8ba-30a0ced40518"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True Positive : 1087\n",
            "True Negative : 1776\n",
            "False Positive : 0\n",
            "False Negative : 0\n",
            "\n",
            "True Positive Rate : 1.0\n",
            "True Negative Rate : 1.0\n",
            "False Positive Rate : 0.0\n",
            "False Negative Rate : 0.0\n",
            "\n",
            "Positive Predictive Value : 1.0\n",
            "Negative predictive value : 1.0\n"
          ]
        }
      ],
      "source": [
        "TN = cm1[0][0]\n",
        "FN = cm1[1][0]\n",
        "TP = cm1[1][1]\n",
        "FP = cm1[0][1]\n",
        "print(\"True Positive :\",TP)\n",
        "print(\"True Negative :\",TN)\n",
        "print(\"False Positive :\",FP)\n",
        "print(\"False Negative :\",FN)\n",
        "print(\"\")\n",
        "TPR = TP/(TP+FN)\n",
        "TNR = TN/(TN+FP)\n",
        "FPR = FP/(FP+TN)\n",
        "FNR = FN/(TP+FN)\n",
        "print(\"True Positive Rate :\",TPR)\n",
        "print(\"True Negative Rate :\",TNR)\n",
        "print(\"False Positive Rate :\",FPR)\n",
        "print(\"False Negative Rate :\",FNR)\n",
        "print(\"\")\n",
        "PPV = TP/(TP+FP)\n",
        "NPV = TN/(TN+FN)\n",
        "print(\"Positive Predictive Value :\",PPV)\n",
        "print(\"Negative predictive value :\",NPV)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFvgkaFmHZkm"
      },
      "source": [
        "Support Vector Machines:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qHP-U-OaHZkm",
        "outputId": "05695660-8c51-4cda-8943-ddadd1f9ba35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Classification report of Support Vector Machines Results:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.90      0.89      1776\n",
            "           1       0.82      0.80      0.81      1087\n",
            "\n",
            "    accuracy                           0.86      2863\n",
            "   macro avg       0.85      0.85      0.85      2863\n",
            "weighted avg       0.86      0.86      0.86      2863\n",
            "\n",
            "Accuracy result of Support Vector Machines is: 85.78414250785889\n",
            "\n",
            "Confusion Matrix result of Support Vector Machines is:\n",
            " [[1591  185]\n",
            " [ 222  865]]\n",
            "\n",
            "Sensitivity :  0.8958333333333334\n",
            "\n",
            "Specificity :  0.795768169273229\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "s = SVC()\n",
        "\n",
        "s.fit(X_train,y_train)\n",
        "\n",
        "predicts = s.predict(X_test)\n",
        "\n",
        "print(\"\")\n",
        "print('Classification report of Support Vector Machines Results:')\n",
        "print(\"\")\n",
        "\n",
        "print(classification_report(y_test,predicts))\n",
        "x = (accuracy_score(y_test,predicts)*100)\n",
        "\n",
        "print('Accuracy result of Support Vector Machines is:', x)\n",
        "print(\"\")\n",
        "cm2=confusion_matrix(y_test,predicts)\n",
        "print('Confusion Matrix result of Support Vector Machines is:\\n',cm2)\n",
        "print(\"\")\n",
        "sensitivity1 = cm2[0,0]/(cm2[0,0]+cm2[0,1])\n",
        "print('Sensitivity : ', sensitivity1 )\n",
        "print(\"\")\n",
        "specificity1 = cm2[1,1]/(cm2[1,0]+cm2[1,1])\n",
        "print('Specificity : ', specificity1)\n",
        "print(\"\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O9CuxLZWHZkp",
        "outputId": "d2b87f3f-da87-4e51-96fb-56cb551f638f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True Positive : 865\n",
            "True Negative : 1591\n",
            "False Positive : 185\n",
            "False Negative : 222\n",
            "\n",
            "True Positive Rate : 0.795768169273229\n",
            "True Negative Rate : 0.8958333333333334\n",
            "False Positive Rate : 0.10416666666666667\n",
            "False Negative Rate : 0.20423183072677092\n",
            "\n",
            "Positive Predictive Value : 0.8238095238095238\n",
            "Negative predictive value : 0.8775510204081632\n"
          ]
        }
      ],
      "source": [
        "TN = cm2[0][0]\n",
        "FN = cm2[1][0]\n",
        "TP = cm2[1][1]\n",
        "FP = cm2[0][1]\n",
        "print(\"True Positive :\",TP)\n",
        "print(\"True Negative :\",TN)\n",
        "print(\"False Positive :\",FP)\n",
        "print(\"False Negative :\",FN)\n",
        "print(\"\")\n",
        "TPR = TP/(TP+FN)\n",
        "TNR = TN/(TN+FP)\n",
        "FPR = FP/(FP+TN)\n",
        "FNR = FN/(TP+FN)\n",
        "print(\"True Positive Rate :\",TPR)\n",
        "print(\"True Negative Rate :\",TNR)\n",
        "print(\"False Positive Rate :\",FPR)\n",
        "print(\"False Negative Rate :\",FNR)\n",
        "print(\"\")\n",
        "PPV = TP/(TP+FP)\n",
        "NPV = TN/(TN+FN)\n",
        "print(\"Positive Predictive Value :\",PPV)\n",
        "print(\"Negative predictive value :\",NPV)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3z2RFHVkHZkq"
      },
      "source": [
        "# Prediction of Crop by Cost production"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pJMWNfOgHZkr"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "var_mod = ['CPPY']\n",
        "le = LabelEncoder()\n",
        "for i in var_mod:\n",
        "    df[i] = le.fit_transform(df[i]).astype(str)\n",
        "df['CPPYPr']= df.CPPY.map({'126':0, '72':0, '200':0, '78':0, '144':0, '212':0, '149':0, '151':0, '178':0, '312':0,\n",
        "       '187':0, '69':0, '205':0, '276':1, '76':0, '134':0, '66':0, '254':1, '150':0, '291':1,\n",
        "       '155':0, '106':0, '87':0, '119':0, '183':0, '170':0, '28':0, '265':1, '180':0, '36':0,\n",
        "       '293':1, '326':1, '148':0, '234':1, '3':0, '124':0, '111':0, '182':0, '166':0, '169':0,\n",
        "       '270':1, '243':1, '98':0, '230':0, '282':1, '334':1, '188':0, '65':0, '34':0, '328':1,\n",
        "       '160':0, '115':0, '89':0, '152':0, '240':1, '141':0, '233':1, '32':0, '281':1, '335':1,\n",
        "       '109':0, '47':0, '136':0, '112':0, '261':1, '229':0, '42':0, '325':1, '213':0, '175':0,\n",
        "       '125':0, '196':0, '292':1, '82':0, '235':1, '79':0, '105':0, '123':0, '210':0, '39':0,\n",
        "       '146':0, '185':0, '201':0, '41':0, '164':0, '184':0, '222':0, '250':0, '301':1,\n",
        "       '218':0, '217':0, '168':0, '264':1, '46':0, '103':0, '284':1, '198':0, '56':0, '171':0,\n",
        "       '331':1, '256':1, '99':0, '58':0, '247':1, '286':1, '67':0, '133':0, '143':0, '204':0,\n",
        "       '227':0, '194':0, '6':0, '280':1, '225':0, '48':0, '258':1, '94':0, '244':1, '294':1,\n",
        "       '215':0, '132':0, '277':1, '71':0, '219':0, '315':1, '97':0, '91':0, '156':0, '289':1,\n",
        "       '100':0, '295':1, '147':0, '214':0, '19':0, '223':0, '90':0, '269':1, '300':1, '114':0,\n",
        "       '135':0, '173':0, '55':0, '113':0, '127':0, '73':0, '177':0, '274':1, '118':0, '191':0,\n",
        "       '145':0, '307':1, '162':0, '228':0, '50':0, '5':0, '248':1, '203':0, '61':0, '129':0,\n",
        "       '192':0, '83':0, '158':0, '206':0, '242':1, '267':1, '137':0, '92':0, '176':0, '298':1,\n",
        "       '25':0, '296':1, '186':0, '239':1, '193':0, '165':0, '310':1, '20':0, '287':1, '75':0,\n",
        "       '107':0, '271':1, '138':0, '121':0, '241':1, '74':0, '43':0, '232':0, '154':0, '181':0,\n",
        "       '195':0, '102':0, '237':1, '110':0, '268':1, '49':0, '104':0, '64':0, '318':1, '13':0,\n",
        "       '128':0, '153':0, '31':0, '93':0, '309':1, '257':0, '52':0, '262':1, '202':0, '174':0,\n",
        "       '101':0, '224':0, '86':0, '45':0, '263':1, '167':0, '17':0, '29':0, '30':0, '54':0,\n",
        "       '303':1, '163':0, '251':1, '142':0, '273':1, '96':0, '35':0, '327':1, '53':0, '15':0,\n",
        "       '190':0, '308':1, '226':0, '139':0, '62':0, '2':0, '1':0, '27':0, '84':0, '285':1,\n",
        "       '246':1, '23':0, '189':0, '299':1, '231':0, '24':0, '16':0, '333':1, '306':1, '18':0,\n",
        "       '288':1, '199':0, '260':1, '323':1, '37':0, '278':1, '324':1, '140':0, '4':0, '245':1,\n",
        "       '322':1, '329':1, '159':0, '80':0, '197':0, '275':1, '11':0, '40':0, '33':0, '0':0,\n",
        "       '320':1, '290':1, '68':0, '220':0, '21':0, '330':1, '12':0, '157':0, '302':1, '44':0,\n",
        "       '221':0, '216':0, '279':1, '63':0, '313':1, '311':1, '332':1, '266':1, '238':1,\n",
        "       '211':0, '172':0, '14':0, '117':0, '161':0, '122':0, '60':0, '321':1, '95':0, '208':0,\n",
        "       '272':1, '131':0, '207':0, '81':0, '314':1, '51':0, '283':1, '120':0, '38':0, '9':0,\n",
        "       '130':0, '70':0, '179':0, '7':0, '10':0, '108':0, '255':1, '77':0, '88':0, '22':0,\n",
        "       '57':0, '85':0, '304':1, '253':1, '249':0, '297':1, '116':0, '305':1, '317':1, '252':1,\n",
        "       '236':1, '209':0, '259':1, '26':0, '319':1, '316':1, '59':0, '336':1, '8':0})\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "efeJcyM8HZkt",
        "outputId": "37ef4068-0ba9-4c99-8615-6d22a5705699"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State_Name</th>\n",
              "      <th>District_Name</th>\n",
              "      <th>Crop_Year</th>\n",
              "      <th>Season</th>\n",
              "      <th>Crop</th>\n",
              "      <th>Area</th>\n",
              "      <th>R</th>\n",
              "      <th>H</th>\n",
              "      <th>T</th>\n",
              "      <th>CC</th>\n",
              "      <th>CP</th>\n",
              "      <th>Y</th>\n",
              "      <th>CPPY</th>\n",
              "      <th>YPr</th>\n",
              "      <th>CPPYPr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
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              "      <td>37</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>26</td>\n",
              "      <td>39</td>\n",
              "      <td>44</td>\n",
              "      <td>23</td>\n",
              "      <td>19</td>\n",
              "      <td>41</td>\n",
              "      <td>232</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>13</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>22</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>27</td>\n",
              "      <td>40</td>\n",
              "      <td>23</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>14</td>\n",
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              "      <td>40</td>\n",
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              "      <td>34</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
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              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>187</td>\n",
              "      <td>47</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>284</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  State_Name District_Name Crop_Year Season Crop Area    R   H   T  CC  CP  \\\n",
              "0          0            37        11      0    0   20  141  40   2  14  24   \n",
              "1          0            37        12      0    0   20   26  39  44  23  19   \n",
              "2          0            37        13      3    0   21   22  41   1  27  40   \n",
              "3          0            37        14      3    0   23   82  40  44  34  30   \n",
              "4          0            37        15      3    0   24  187  47   7  13  17   \n",
              "\n",
              "    Y CPPY  YPr  CPPYPr  \n",
              "0  44  235    0       0  \n",
              "1  41  232    0       1  \n",
              "2  23   18    0       0  \n",
              "3  28  239    0       1  \n",
              "4   8  284    0       0  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "var_mod = ['State_Name', 'District_Name', 'Crop_Year', 'Season', 'Crop', 'Area',\n",
        "       'R', 'H', 'T', 'CC', 'CP', 'Y','CPPY']\n",
        "le = LabelEncoder()\n",
        "for i in var_mod:\n",
        "    df[i] = le.fit_transform(df[i]).astype(str)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lPJcl57hHZkv"
      },
      "outputs": [],
      "source": [
        "#According to the cross-validated MCC scores, the random forest is the best-performing model, so now let's evaluate its performance on the test set.\n",
        "from sklearn.metrics import confusion_matrix, classification_report, matthews_corrcoef, cohen_kappa_score, accuracy_score, average_precision_score, roc_auc_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YBjOSsqaHZkv"
      },
      "outputs": [],
      "source": [
        "X = df.drop(labels='CPPYPr', axis=1)\n",
        "#Response variable\n",
        "y = df.loc[:,'CPPYPr']    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OngdNGlEHZkw"
      },
      "outputs": [],
      "source": [
        "\n",
        "#We'll use a test size of 30%. We also stratify the split on the response variable, which is very important to do because there are so few fraudulent transactions.\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oMlm1CwSHZkx"
      },
      "source": [
        "Random Forest:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nuwr03iNHZkx",
        "outputId": "378854b5-42c7-4e1d-ae29-a07d92f6ff40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Classification report of Random Forest Results:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00      1986\n",
            "           1       1.00      1.00      1.00       877\n",
            "\n",
            "    accuracy                           1.00      2863\n",
            "   macro avg       1.00      1.00      1.00      2863\n",
            "weighted avg       1.00      1.00      1.00      2863\n",
            "\n",
            "Accuracy result of Random Forest is: 100.0\n",
            "\n",
            "Confusion Matrix result of Random Forest is:\n",
            " [[1986    0]\n",
            " [   0  877]]\n",
            "\n",
            "Sensitivity :  1.0\n",
            "\n",
            "Specificity :  1.0\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc = RandomForestClassifier()\n",
        "\n",
        "rfc.fit(X_train,y_train)\n",
        "\n",
        "predictR = rfc.predict(X_test)\n",
        "\n",
        "print(\"\")\n",
        "print('Classification report of Random Forest Results:')\n",
        "print(\"\")\n",
        "\n",
        "print(classification_report(y_test,predictR))\n",
        "x = (accuracy_score(y_test,predictR)*100)\n",
        "\n",
        "print('Accuracy result of Random Forest is:', x)\n",
        "print(\"\")\n",
        "cm1=confusion_matrix(y_test,predictR)\n",
        "print('Confusion Matrix result of Random Forest is:\\n',cm1)\n",
        "print(\"\")\n",
        "sensitivity1 = cm1[0,0]/(cm1[0,0]+cm1[0,1])\n",
        "print('Sensitivity : ', sensitivity1 )\n",
        "print(\"\")\n",
        "specificity1 = cm1[1,1]/(cm1[1,0]+cm1[1,1])\n",
        "print('Specificity : ', specificity1)\n",
        "print(\"\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L_QlXepjHZky",
        "outputId": "3ce89a6c-d352-40c5-e0c5-7f8855bb08a5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True Positive : 877\n",
            "True Negative : 1986\n",
            "False Positive : 0\n",
            "False Negative : 0\n",
            "\n",
            "True Positive Rate : 1.0\n",
            "True Negative Rate : 1.0\n",
            "False Positive Rate : 0.0\n",
            "False Negative Rate : 0.0\n",
            "\n",
            "Positive Predictive Value : 1.0\n",
            "Negative predictive value : 1.0\n"
          ]
        }
      ],
      "source": [
        "TN = cm1[0][0]\n",
        "FN = cm1[1][0]\n",
        "TP = cm1[1][1]\n",
        "FP = cm1[0][1]\n",
        "print(\"True Positive :\",TP)\n",
        "print(\"True Negative :\",TN)\n",
        "print(\"False Positive :\",FP)\n",
        "print(\"False Negative :\",FN)\n",
        "print(\"\")\n",
        "TPR = TP/(TP+FN)\n",
        "TNR = TN/(TN+FP)\n",
        "FPR = FP/(FP+TN)\n",
        "FNR = FN/(TP+FN)\n",
        "print(\"True Positive Rate :\",TPR)\n",
        "print(\"True Negative Rate :\",TNR)\n",
        "print(\"False Positive Rate :\",FPR)\n",
        "print(\"False Negative Rate :\",FNR)\n",
        "print(\"\")\n",
        "PPV = TP/(TP+FP)\n",
        "NPV = TN/(TN+FN)\n",
        "print(\"Positive Predictive Value :\",PPV)\n",
        "print(\"Negative predictive value :\",NPV)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ln4LYUp-HZkz"
      },
      "source": [
        "Support Vector Machines:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7pilKPzfHZk0",
        "outputId": "6ba7d531-dc28-49d6-e0fa-81c935a160c6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Classification report of Support Vector Machines Results:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      1.00      0.82      1986\n",
            "           1       0.00      0.00      0.00       877\n",
            "\n",
            "    accuracy                           0.69      2863\n",
            "   macro avg       0.35      0.50      0.41      2863\n",
            "weighted avg       0.48      0.69      0.57      2863\n",
            "\n",
            "Accuracy result of Support Vector Machines is: 69.36779601816276\n",
            "\n",
            "Confusion Matrix result of Support Vector Machines is:\n",
            " [[1986    0]\n",
            " [ 877    0]]\n",
            "\n",
            "Sensitivity :  1.0\n",
            "\n",
            "Specificity :  0.0\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\SPIRO-PYTHON\\anaconda3_\\envs\\UDI\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "C:\\Users\\SPIRO-PYTHON\\anaconda3_\\envs\\UDI\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "C:\\Users\\SPIRO-PYTHON\\anaconda3_\\envs\\UDI\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "s = SVC()\n",
        "\n",
        "s.fit(X_train,y_train)\n",
        "\n",
        "predicts = s.predict(X_test)\n",
        "\n",
        "print(\"\")\n",
        "print('Classification report of Support Vector Machines Results:')\n",
        "print(\"\")\n",
        "\n",
        "print(classification_report(y_test,predicts))\n",
        "x = (accuracy_score(y_test,predicts)*100)\n",
        "\n",
        "print('Accuracy result of Support Vector Machines is:', x)\n",
        "print(\"\")\n",
        "cm2=confusion_matrix(y_test,predicts)\n",
        "print('Confusion Matrix result of Support Vector Machines is:\\n',cm2)\n",
        "print(\"\")\n",
        "sensitivity1 = cm2[0,0]/(cm2[0,0]+cm2[0,1])\n",
        "print('Sensitivity : ', sensitivity1 )\n",
        "print(\"\")\n",
        "specificity1 = cm2[1,1]/(cm2[1,0]+cm2[1,1])\n",
        "print('Specificity : ', specificity1)\n",
        "print(\"\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yMXyjMUzHZk1",
        "outputId": "d314f5a9-7ac3-4768-f79e-c445df00f454"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "True Positive : 0\n",
            "True Negative : 1986\n",
            "False Positive : 0\n",
            "False Negative : 877\n",
            "\n",
            "True Positive Rate : 0.0\n",
            "True Negative Rate : 1.0\n",
            "False Positive Rate : 0.0\n",
            "False Negative Rate : 1.0\n",
            "\n",
            "Positive Predictive Value : nan\n",
            "Negative predictive value : 0.6936779601816276\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\SPIRO-~1\\AppData\\Local\\Temp/ipykernel_7416/170764816.py:19: RuntimeWarning: invalid value encountered in longlong_scalars\n",
            "  PPV = TP/(TP+FP)\n"
          ]
        }
      ],
      "source": [
        "TN = cm2[0][0]\n",
        "FN = cm2[1][0]\n",
        "TP = cm2[1][1]\n",
        "FP = cm2[0][1]\n",
        "print(\"True Positive :\",TP)\n",
        "print(\"True Negative :\",TN)\n",
        "print(\"False Positive :\",FP)\n",
        "print(\"False Negative :\",FN)\n",
        "print(\"\")\n",
        "TPR = TP/(TP+FN)\n",
        "TNR = TN/(TN+FP)\n",
        "FPR = FP/(FP+TN)\n",
        "FNR = FN/(TP+FN)\n",
        "print(\"True Positive Rate :\",TPR)\n",
        "print(\"True Negative Rate :\",TNR)\n",
        "print(\"False Positive Rate :\",FPR)\n",
        "print(\"False Negative Rate :\",FNR)\n",
        "print(\"\")\n",
        "PPV = TP/(TP+FP)\n",
        "NPV = TN/(TN+FN)\n",
        "print(\"Positive Predictive Value :\",PPV)\n",
        "print(\"Negative predictive value :\",NPV)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hj6qjEAeHZk2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JqL-zPKZHZk2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1o4lUMoXHZk2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PjtCBOTGHZk2"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3wAJHx61HZk3"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t_CGhLoiHZk3"
      },
      "outputs": [],
      "source": [
        ""
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
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